PART 01 LangChain Overview

001 What is LangChain?
002 Understanding LangChain Expression Language (LCEL)
003 LangChain Main Components

PART 02 Chat Model Providers and Usage Methods

001 OpenAI
002 Anthropic
003 Google
004 Groq
005 Ollama

PART 03 Creating a Simple Chatbot

001 Basic Chatbot Implementation
002 Adding Memory to a Chatbot

PART 04 Understanding and Implementing RAG Concepts

001 RAG (Retrieval-Augmented Generation) concept
002 Document loading and splitting
003 Embedding Model
004 Building a vector repository
005 Q&A RAG Chain Implementation

PART 05 Tool Calling

001 The concept and role of tools
002 Using LangChain's built-in tools
003 Creating a Custom Tool
004 Utilizing Structured Output

PART 06 Agent

001 Understanding the Agent Concept
002 Basic Agent Implementation
Added memory function to the 003 agent execution tool.
004 LangGraph Agent Implementation
005 Agent-based RAG Chatbot Implementation

PART 07 Practical Project

001 News Analysis Project Using LangChain
002 Open source local RAG implementation using Ollama
003 Data Analysis and Code Execution Agent

From RAG chatbots to agents, learn everything about Langchain through step-by-step practice!

LangChain is an open-source framework for building applications based on large-scale language models (LLMs).
In particular, Langchain has great potential for future use, as it goes beyond simply using LLM and allows for flexible connection of LLM with various data sources, tools, and external systems through its unique scalability.
Continuous learning and practice are essential to keep pace with the rapidly changing trends in AI and LLM technology. However, if you start without any prior knowledge, you are likely to encounter difficulties due to complex structures and conceptual barriers.
Therefore, 『Introduction to Langchain』 is designed to be easily understandable even for beginners by explaining the chatbot-style conversational RAG system, the construction process, and the overall flow of LLM-based applications, and is structured so that even beginners can follow along through practice.

In this book, you can learn how to develop a conversational chatbot using a chat model, implement a question-and-answer RAG Chain, utilize LangChain's built-in tools and create custom tools, implement agents and build an agent-based RAG chatbot, and complete three practical projects.
All examples are organized step by step so that even beginners can easily understand them.
We have structured the program so that you can first understand the concept learning and implementation goals, sequentially execute examples according to your goals, and check the execution results and code explanations, allowing you to systematically understand and practice.
This book will be helpful for those who want to understand the basic grammar and LLM of Langchain, especially those who want to directly integrate generative AI into their services and build chatbots and RAG systems.